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  "metadata": {
    "colab": {
      "name": "Big_Mart Sales Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN4dkdZUhZjHNuXRQtFUVjg",
      "include_colab_link": true
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      "name": "python3",
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    "language_info": {
      "name": "python"
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SarraChebaane/Machine-Learning-Projects-for-Beginners/blob/main/Big_Mart_Sales_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 196,
      "metadata": {
        "id": "7-PiZPV4aMzl"
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Collection"
      ],
      "metadata": {
        "id": "Cz5gmWMwa7-o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the dataset from csv file to a Pandas DataFrame\n",
        "\n",
        "big_mart_data = pd.read_csv('/content/Train.csv')"
      ],
      "metadata": {
        "id": "-7K0dKuNa-IR"
      },
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# first 5 rows of the DataFrame \n",
        "\n",
        "big_mart_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "q_YBi6--bepL",
        "outputId": "6f3b656f-c2c8-48ff-ceec-6f62df25f41e"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
              "0           FDA15         9.30          Low Fat         0.016047   \n",
              "1           DRC01         5.92          Regular         0.019278   \n",
              "2           FDN15        17.50          Low Fat         0.016760   \n",
              "3           FDX07        19.20          Regular         0.000000   \n",
              "4           NCD19         8.93          Low Fat         0.000000   \n",
              "\n",
              "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
              "0                  Dairy  249.8092            OUT049   \n",
              "1            Soft Drinks   48.2692            OUT018   \n",
              "2                   Meat  141.6180            OUT049   \n",
              "3  Fruits and Vegetables  182.0950            OUT010   \n",
              "4              Household   53.8614            OUT013   \n",
              "\n",
              "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
              "0                       1999      Medium               Tier 1   \n",
              "1                       2009      Medium               Tier 3   \n",
              "2                       1999      Medium               Tier 1   \n",
              "3                       1998         NaN               Tier 3   \n",
              "4                       1987        High               Tier 3   \n",
              "\n",
              "         Outlet_Type  Item_Outlet_Sales  \n",
              "0  Supermarket Type1          3735.1380  \n",
              "1  Supermarket Type2           443.4228  \n",
              "2  Supermarket Type1          2097.2700  \n",
              "3      Grocery Store           732.3800  \n",
              "4  Supermarket Type1           994.7052  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-029a589d-2e91-453b-8dfc-9094b15f1e5a\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.30</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.50</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.93</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# number of data points and number of feature\n",
        "big_mart_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRo33liZdnj6",
        "outputId": "1713a792-fbdb-449b-d8d0-c92c8022db76"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8523, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting some information about the dataset\n",
        "big_mart_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUE2OJHsdzcn",
        "outputId": "a2f2cc8c-e56b-4f34-8c9b-f675d32c9b3f"
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8523 entries, 0 to 8522\n",
            "Data columns (total 12 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   Item_Identifier            8523 non-null   object \n",
            " 1   Item_Weight                7060 non-null   float64\n",
            " 2   Item_Fat_Content           8523 non-null   object \n",
            " 3   Item_Visibility            8523 non-null   float64\n",
            " 4   Item_Type                  8523 non-null   object \n",
            " 5   Item_MRP                   8523 non-null   float64\n",
            " 6   Outlet_Identifier          8523 non-null   object \n",
            " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
            " 8   Outlet_Size                6113 non-null   object \n",
            " 9   Outlet_Location_Type       8523 non-null   object \n",
            " 10  Outlet_Type                8523 non-null   object \n",
            " 11  Item_Outlet_Sales          8523 non-null   float64\n",
            "dtypes: float64(4), int64(1), object(7)\n",
            "memory usage: 799.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Categorical features: \n",
        "- Item_identifier\n",
        "- Item_fat_content\n",
        "- Item_Type\n",
        "- Outlet_Identifier\n",
        "- Outlet_Size\n",
        "- Outlet_Location_Type\n",
        "- Outlet_Type"
      ],
      "metadata": {
        "id": "OCj9-s1PeK_p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for missing values\n",
        "big_mart_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFa9Yt0weypF",
        "outputId": "cadff0f9-e8f7-4d96-839b-f50402fbe169"
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                  1463\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 0\n",
              "Item_Type                       0\n",
              "Item_MRP                        0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Size                  2410\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Type                     0\n",
              "Item_Outlet_Sales               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handling Missing Values"
      ],
      "metadata": {
        "id": "JzdLAhHzfhv8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mean --> average value\n",
        "Mode --> Most repeated value"
      ],
      "metadata": {
        "id": "um9FUJysfk0D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# mean value of \"item_Weight\" column\n",
        "\n",
        "big_mart_data['Item_Weight'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pnh156jzfbT-",
        "outputId": "c17622bd-45a8-48e6-b5e0-82cff7e19f01"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.857645184135976"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# filling the missing values in \"Item_Weight\" column with \"Mean\" value\n",
        "\n",
        "big_mart_data['Item_Weight'].fillna(big_mart_data['Item_Weight'].mean(),inplace = True)"
      ],
      "metadata": {
        "id": "wmlFxYs0gPEy"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for missing values\n",
        "big_mart_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ox645JGXgyDj",
        "outputId": "559a7c6f-b689-4748-8346-956e5c677525"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                     0\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 0\n",
              "Item_Type                       0\n",
              "Item_MRP                        0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Size                  2410\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Type                     0\n",
              "Item_Outlet_Sales               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Replacing the missing values in \"Outlet_Size\" with mode"
      ],
      "metadata": {
        "id": "YBwldHo3g3_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mode_of_outlet_size = big_mart_data.pivot_table(values='Outlet_Size',columns = \"Outlet_Type\", aggfunc = (lambda x: x.mode()[0]))"
      ],
      "metadata": {
        "id": "aPwy0Wf2g9Ns"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(mode_of_outlet_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkOxfHhKpKqw",
        "outputId": "0d50c93b-4e64-4982-d8c0-8adfa967fae4"
      },
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2  \\\n",
            "Outlet_Size         Small             Small            Medium   \n",
            "\n",
            "Outlet_Type Supermarket Type3  \n",
            "Outlet_Size            Medium  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = big_mart_data['Outlet_Size'].isnull()"
      ],
      "metadata": {
        "id": "adaeKsiYpeq4"
      },
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(missing_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2ylysYXpwRk",
        "outputId": "29f02bee-2e92-4490-876b-7da2fc8b60d2"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       False\n",
            "1       False\n",
            "2       False\n",
            "3        True\n",
            "4       False\n",
            "        ...  \n",
            "8518    False\n",
            "8519     True\n",
            "8520    False\n",
            "8521    False\n",
            "8522    False\n",
            "Name: Outlet_Size, Length: 8523, dtype: bool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "big_mart_data.loc[miss_values, 'Outlet_Size'] = big_mart_data.loc[miss_values,'Outlet_Type'].apply(lambda x: mode_of_Outlet_size[x])"
      ],
      "metadata": {
        "id": "8m7G6OvPqMlG"
      },
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for missing values\n",
        "big_mart_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWEPQhz6rG5e",
        "outputId": "9709b323-1f62-42c3-9cb9-f3f245e3c0d7"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                     0\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 0\n",
              "Item_Type                       0\n",
              "Item_MRP                        0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Size                  2410\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Type                     0\n",
              "Item_Outlet_Sales               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Analysis"
      ],
      "metadata": {
        "id": "ARhXMIFRrNAK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# statistical measures about the data\n",
        "big_mart_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "rZ-HWoeOrPYJ",
        "outputId": "4fbd3457-98f9-4565-83f7-e1c05498073b"
      },
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
              "count  8523.000000      8523.000000  8523.000000                8523.000000   \n",
              "mean     12.857645         0.066132   140.992782                1997.831867   \n",
              "std       4.226124         0.051598    62.275067                   8.371760   \n",
              "min       4.555000         0.000000    31.290000                1985.000000   \n",
              "25%       9.310000         0.026989    93.826500                1987.000000   \n",
              "50%      12.857645         0.053931   143.012800                1999.000000   \n",
              "75%      16.000000         0.094585   185.643700                2004.000000   \n",
              "max      21.350000         0.328391   266.888400                2009.000000   \n",
              "\n",
              "       Item_Outlet_Sales  \n",
              "count        8523.000000  \n",
              "mean         2181.288914  \n",
              "std          1706.499616  \n",
              "min            33.290000  \n",
              "25%           834.247400  \n",
              "50%          1794.331000  \n",
              "75%          3101.296400  \n",
              "max         13086.964800  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13e9219d-dc27-43d7-b946-aecf63334143\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.857645</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>140.992782</td>\n",
              "      <td>1997.831867</td>\n",
              "      <td>2181.288914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.226124</td>\n",
              "      <td>0.051598</td>\n",
              "      <td>62.275067</td>\n",
              "      <td>8.371760</td>\n",
              "      <td>1706.499616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.555000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.290000</td>\n",
              "      <td>1985.000000</td>\n",
              "      <td>33.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.310000</td>\n",
              "      <td>0.026989</td>\n",
              "      <td>93.826500</td>\n",
              "      <td>1987.000000</td>\n",
              "      <td>834.247400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.857645</td>\n",
              "      <td>0.053931</td>\n",
              "      <td>143.012800</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>1794.331000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.094585</td>\n",
              "      <td>185.643700</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>3101.296400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21.350000</td>\n",
              "      <td>0.328391</td>\n",
              "      <td>266.888400</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>13086.964800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13e9219d-dc27-43d7-b946-aecf63334143')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-13e9219d-dc27-43d7-b946-aecf63334143 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-13e9219d-dc27-43d7-b946-aecf63334143');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Numerical Features"
      ],
      "metadata": {
        "id": "c21U9vwCu6a7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set()"
      ],
      "metadata": {
        "id": "QFKF778Du2j9"
      },
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# item_weight distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.displot(big_mart_data['Item_Weight'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "L6LJUy7AvBF4",
        "outputId": "e7cb2e99-2ddc-4e13-e9ff-43cd712091b7"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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99RSysrJQVlYGs9kMADCbzSgrK8Mrr7yCRYsWuTwkEdFQYPdtYQkJCaitrcXq1avR1tYGjUaDa9euwdvbG8899xzi4+Nl5CQiGvQceh/uokWLkJiYiBMnTuDatWvQaDSYMGEC/Pz8XJ2PiGjIcPiDD35+fjbfrUBERPYN6Ct2iIjIcSxcIiJJHB5ScJVvv/22y/eiNTY2oqmpCR9//DGio6OhVqvh4+MDAEhLS7MMa5w8eRKZmZm4desWQkJCkJeXh8DAQLc8ByIiR7i9cEeOHIni4mLL/Q0bNsBkMlnub9q0CWFhYV0eYzabkZ6ejpycHBgMBmzevBn5+fnIycmRlpuIqK8UNaTQ2tqKkpISzJ07t9flKioq4OPjA4PBAABISkrC/v37ZUQkIuo3t5/hdnbw4EEEBQVh3LhxlmlpaWkQQmDSpElYuXIlhg0bhurqagQHB1uWCQgIgNlstrxljYhIiRRVuDt27OhydltYWAidTofW1lZs2LAB2dnZyM/Pd9r2AgOd9z5irdbfaetyJuayz9vby+rtztyZV0n7qjPm6jvFFG5NTQ3Ky8uRm5trmabT6QAAarUaycnJloud63Q6VFVVWZarr6+HSqXq89ltXV0TzOaBf0WQVuuPq1cbB7weZ2Mu+7Raf7S1tQPoKNvbt7tzV14l7avOmKv3DLYoZgx3586dmDp1quVLKpubm9HY2LHjhBDYu3cv9Ho9ACAiIgItLS04duwYAKCoqIhf9UNEiqeYM9ydO3ciIyPDcr+urg6pqakwmUwwm80YO3YssrKyAAAqlQq5ubnIysrq8rYwIiIlU0zhlpaWdrk/atQo7Nq1y+byEydORElJiatjERE5jWKGFIiIhjoWLhGRJCxcIiJJWLhERJKwcImIJGHhEhFJwsIlIpKEhUtEJAkLl4hIEhYuEZEkLFwiIklYuEREkrBwiYgkYeESEUnCwiUikoSFS0QkCQuXiEgSFi4RkSQsXCIiSVi4RESSsHCJiCRh4RIRScLCJSKShIVLRCQJC5eISBIWLhGRJCxcIiJJWLhERJKwcImIJGHhEhFJwsIlIpKEhUtEJImXuwMAQHR0NNRqNXx8fAAAaWlpiIqKwsmTJ5GZmYlbt24hJCQEeXl5CAwMBIBe5xERKZFiznA3bdqE4uJiFBcXIyoqCmazGenp6cjMzERpaSkMBgPy8/MBoNd5RERKpZjC7a6iogI+Pj4wGAwAgKSkJOzfv9/uPCIipVLEkALQMYwghMCkSZOwcuVKVFdXIzg42DI/ICAAZrMZ165d63WeRqNxR3wiIrsUUbiFhYXQ6XRobW3Fhg0bkJ2djdjYWJdvNzDQz2nr0mr9nbYuZ2Iu+7y9vaze7sydeZW0rzpjrr5TROHqdDoAgFqtRnJyMpYtW4aFCxeiqqrKskx9fT1UKhU0Gg10Op3NeX1RV9cEs1kMOL9W64+rVxsHvB5nYy77tFp/tLW1A+go29u3u3NXXiXtq86Yq/cMtrh9DLe5uRmNjR07SAiBvXv3Qq/XIyIiAi0tLTh27BgAoKioCHFxcQDQ6zwiIqVy+xluXV0dUlNTYTKZYDabMXbsWGRlZUGlUiE3NxdZWVld3voFoNd5RERK5fbCHTVqFHbt2mV13sSJE1FSUtLneURESuT2IQUiojsFC5eISBIWLhGRJCxcIiJJWLhERJKwcImIJGHhEhFJwsIlIpKEhUtEJAkLl4hIEhYuEZEkLFwiIklYuEREkrBwiYgkYeESEUnCwiUikoSFS0Qkidu/8YHkGfY9X/io7b/kt1rbcf27mxISEd1ZWLhDhK0y7f4Noul/PGx3XXkrpjotFxH9PxbuEOGj9upRpt2/9ptFSuReLFzqNw5R3Lna2s09/nqyhq99Vyxc6jdrZ9XW8Mx66PH2UvG17we+S4GISBKe4VIPjv65SKRESh7qYuFSD/xzkQYzJQ91sXAVztHf1kS9UfJZ352EP8kKp+Tf1jR48DhSBhYukUIN+54vgJ4fXumOZ6WDBwvXTThUQPb4qL3w0uZ/d/nwijU8Kx08+BPvJvwTr38c/UXV1m6Ct5enhETux3eVDB4sXBpU+vKL6k75hebIu0qGwvMcCvjBByIiSdx+htvQ0IBVq1bhwoULUKvVGD16NLKzsxEQEIDw8HCEhYVBper4vZCbm4vw8HAAwMGDB5GbmwuTyYRx48YhJycHvr6+7nwqdIfjW6/6507ab24vXA8PD6SkpGDy5MkAAKPRiPz8fLz66qsAgKKiItxzzz1dHnPjxg28/PLLKCwsRGhoKDIyMvDOO+/gd7/7nfT8RLdxXL4nR8eX75T95vbC1Wg0lrIFgPHjx2P79u29PubIkSOIiIhAaGgoACApKQlr1qxh4SqUtR+67veHwtkL9cTx5a7cXridmc1mbN++HdHR0ZZpCxYsgMlkwsMPP4zU1FSo1WpUV1cjODjYskxwcDCqq6v7vL3AQD+n5Absv1fSGm9vx3b/QJbrPk3GNnss46XCS5v/3esya595wOF96MrnYOsxzs7Wl+PFoX3spGUA5z9XJWeT/e4ORRXuunXrcPfdd2P+/PkAgEOHDkGn06GpqQnp6ekoKCjA888/77Tt1dU1wWwWA16PVuuPq1cb+/wYe++vvK2/y3W/APlA1uXM5azl6sv1G1yVzVqu2xx5ffvymjq6vs75euOsZQDnP1eZ2fqyzxxZX3/0VuKKKVyj0Yjz589jy5Ytln+S6XQ6AICfnx8SExOxbds2y/SPPvrI8tiqqirLskTOxve5krMoonA3btyIiooKvP3221Cr1QCA7777Dj4+PrjrrrvQ3t6O0tJS6PV6AEBUVBTWrVuHc+fOITQ0FEVFRZg5c6Y7nwINYbx6mjIMhV98bi/cM2fOYOvWrQgNDUVSUhIAYOTIkUhJSUFmZiY8PDzQ3t6OCRMmYMWKFQA6znizs7OxZMkSmM1m6PV6ZGRkuPNpEJGLOfKLz9vbC68++6CkRH3n9sK9//778eWXX1qdV1JSYvNx06ZNw7Rp01wVi4jI6dxeuER3mqHwpzH1DwuXSDKOCd+5WLhOxssuEpEtbAYn48c7aTDjcIdrsXCJyILDHa7FyzMSEUnCwiUikoSFS0QkCQuXiEgSFi4RkSQsXCIiSVi4RESSsHCJiCRh4RIRScJPmhHRHcnRjzE78wtOWbhEdEdyx8eYOaRARCQJC5eISBIWLhGRJBzDdZC9C4vzGqJEZA8L10G9XVjc29sLbW3tAHidUCKyjUMKRESSsHCJiCRh4RIRScLCJSKShIVLRCQJC5eISBIWLhGRJCxcIiJJWLhERJKwcImIJGHhEhFJMqgL9+zZs5g3bx5mzJiBefPm4dy5c+6ORERk06Au3KysLCQnJ6O0tBTJycnIzMx0dyQiIpsG7dXC6urqcOrUKWzbtg0AEB8fj3Xr1qG+vh4BAQEOrUOl8ujTNof7+1id7uXthfY2T7vLObo+Zy3XPZdSslnLpYRstnK5M5vG38dmpr6uz937V8a6vLy9nLq+2/raFbZ4CCGEU9YkWUVFBVavXo09e/ZYps2aNQt5eXkYN26cG5MREVk3qIcUiIgGk0FbuDqdDjU1NTCZTAAAk8mEK1euQKfTuTkZEZF1g7ZwAwMDodfrsXv3bgDA7t27odfrHR6/JSKSbdCO4QJAZWUl1qxZg+vXr2PYsGEwGo0YM2aMu2MREVk1qAuXiGgwGbRDCkREgw0Ll4hIEhYuEZEkLFwiIkkG7Ud7ZYuOjoZarYaPT8dHAdPS0hAVFdVlmZs3b+LFF1/E559/Dk9PT6xevRqPPPKIyzJ9++23WL58ueV+Y2Mjmpqa8PHHH3dZ7o033sBf//pXjBgxAgAwceJEZGVlOTWL0WhEaWkpLl26hJKSEoSFhQHouMDQmjVrcO3aNWg0GhiNRoSGhvZ4vMlkwvr163H06FF4eHjgmWeeQWJioktyNTQ0YNWqVbhw4QLUajVGjx6N7Oxsq28pXLNmDT788EMMHz4cABAXF4dly5a5JBfg2HEGuOZYs5bJ0WMMcN1x1tvrdfLkSWRmZuLWrVsICQlBXl4eAgMDe6xD9s+mTYIc8sgjj4gvv/yy12XeeOMNkZGRIYQQ4uzZs2LKlCmiqalJRjwhhBDr168Xa9eu7TF906ZN4rXXXnPptsvLy0VVVVWP/bRgwQKxa9cuIYQQu3btEgsWLLD6+J07d4rFixcLk8kk6urqRFRUlLh48aJLcjU0NIj//ve/lmVee+018eKLL1p9/OrVq8Wf//znAedwJJcQjh1nQrjmWLOVqTNbx5gQrjvObL1eJpNJTJs2TZSXlwshhCgoKBBr1qyxug53/2zexiEFJ9q3bx/mzZsHAAgNDUVERASOHDkiZdutra0oKSnB3LlzpWyvO4PB0ONTfrcvMBQfHw+g4wJDp06dQn19fY/H7927F4mJiVCpVAgICMC0adOwf/9+l+TSaDSYPHmy5f748eNRVVU14G0NNFdfuOJYs5fJXceYrderoqICPj4+MBgMAICkpCSbx4w7fzY7Y+H2QVpaGhISEvDKK6/g+vXrPeZXVVUhJCTEcl+n0+Hy5ctSsh08eBBBQUE2L9yzZ88eJCQkYPHixThx4oSUTNXV1QgKCoKnZ8fVrjw9PTFixAhUV1dbXTY4ONhyX9a+M5vN2L59O6Kjo20us23bNiQkJODZZ59FZWWlyzPZO84A9xxr9o4xwPXHWefXq/sxExAQALPZjGvXrvV4nDt/Njtj4TqosLAQ7733Hnbs2AEhBLKzs90dqYsdO3bYPPNISkrCv/71L5SUlODpp5/Gs88+i4aGBskJlWndunW4++67MX/+fKvzn3/+eRw4cAAlJSWYPn06UlJSLNfvcAUlH2e9HWOAnOPM3uuldCxcB93+U0utViM5ORmffPJJj2WCg4Nx6dIly/3q6mp8//vfd3m2mpoalJeXIyEhwep8rVYLb29vAMCDDz4InU6HM2fOuDxXXy4wpNPpuvxZL2PfGY1GnD9/Hq+//jpUKus/CkFBQZZ5jz32GJqbm116ZuTIcQbIP9bsHWOA64+z7q9X92Omvr4eKpUKGo2mx2Pd9bPZHQvXAc3NzWhsbAQACCGwd+9e6PX6HsvFxcXhb3/7GwDg3Llz+Oyzz6z+h9nZdu7cialTp1r+k95dTU2N5fbp06dx6dIl/OAHP3B5rr5cYCguLg7/+Mc/YDabUV9fj/fffx8zZsxwWbaNGzeioqICBQUFUKvVNpfrvO+OHj0KlUqFoKAgl2Ry9DgD5B9r9o4xwLXHmbXXKyIiAi0tLTh27BgAoKioCHFxcVYf766fzR6k/5tuELpw4YKYM2eOiI+PF7NmzRKpqamipqZGCCHE7NmzxeXLl4UQQty4cUOkpqaKadOmienTp4sDBw5IyTd9+nRx+PDhLtNSUlLEp59+KoQQYtWqVeLRRx8VCQkJ4vHHHxeHDh1yeoZ169aJqKgoodfrxZQpU8SsWbOEEEJ8/fXX4oknnhDTp08XTzzxhKisrLSasb29XWRmZoqYmBgRExMjioqKXJbrq6++EmFhYWL69Oli9uzZYvbs2eLZZ5+1PKbza/qb3/xGxMfHi4SEBPHkk0+KEydOuCxXb8dZ91yuONZsvYZCWD/GhJBznPX2eh0/flzEx8eL2NhY8dRTT4mrV69aHqeEn83uePEaIiJJOKRARCQJC5eISBIWLhGRJCxcIiJJWLhERJKwcIlcaMKECbh48aJDy4aHh+P8+fMuTkTuxMIlxYmOjsaHH36If/7zn3jyySelbvvKlSsIDw9HbW2tZdpbb71lddrTTz9td30nTpzAqFGjBpzLHfuCnI+FS9TJiBEjMHr0aJSXl1umHTt2DGPGjOkxLTIy0h0RaRBj4ZIiVVZWIisrCydPnsSECRMsl+BrbW2F0WjEL37xC0yZMgWZmZloaWkBAHz00Ud4+OGH8ac//QkPPPAAHnroIbz//vs4fPgwZsyYgZ/97GfYsmWL3W0bDAbLx0VNJhM+//xzLFy4sMu0EydOWDK9++67mDlzJiIjI/H00093+cx+52GChoYGLF26FBMnTsTcuXPxhz/8ocdZ64cffojp06fDYDBg7YrTQtgAAAOuSURBVNq1EELY3Bc0+LBwSZHGjh2LtWvXYvz48Thx4oSl7PLz83H27Fns2rULZWVluHLlCgoKCiyPq62txa1bt3DkyBE899xz+P3vf2+5+lZhYSE2b95sd0w1MjLScjZ76tQpjB07Fg888ECXae3t7fjJT36C999/H1u3bsWbb76J//znP5g0aRJeeOEFq+vNzs6Gr68v/v3vf8NoNGLXrl09ljl06BDeffddvPfee9i3bx+OHj1qc1/Q4MPCpUFDCIG///3veOmll6DRaODn54clS5Zgz549lmW8vLywbNkyeHt7Y9asWWhoaMDChQvh5+eH+++/H/fddx++/PLLXrcTGRmJM2fO4Pr16zh+/DgMBgNCQ0NRX19vmfbTn/4UarUaRUVFeOaZZzB27Fh4eXlh6dKllgu3dGYymVBWVobU1FT4+vrivvvuw2OPPdZj27/97W8xbNgwBAcHY/Lkyfjiiy+cs/NIEfidZjRo1NfX4+bNm3j88cct04QQMJvNlvsajcZywfO77roLALp8x5WPjw9u3LjR63ZGjhyJoKAgHDt2DOXl5ZZvCpg4caJl2u3x26qqKrz66qswGo1dMtXU1HS54HV9fT3a29u7XJrS2mUqtVqt5bavr6/drDS4sHBJsTw8PLrcHz58OO666y7s2bPHZZdIvM1gMKC8vBwnT560lOmkSZNQXl6O48ePWy6ArdPpsHTpUsyePbvX9QUEBMDLywuXL1+2XLLQ2jdf2NJ9X9DgxCEFUqzAwEDU1NSgtbUVAKBSqZCYmIhXX30VdXV1ADquwXr06FGnbzsyMhLFxcUYMWIE/Pz8AHQUbnFxMZqamjB+/HgAHd9y8Pbbb1sutN3Y2Ih9+/b1WJ+npydiY2Px5ptv4ubNm6isrERxcbHDebrvCxqcWLikWD//+c9x33334aGHHrJ8iWB6ejpGjx6NX/3qV5g4cSKeeuopnD171unbjoyMRF1dHSZNmmSZptfr0dLSgnHjxsHX1xcAEBsbi5SUFKxcuRITJ05EfHy8zS8nzMzMRGNjIx588EGsWrUKjz76aK8XP+/M2r6gwYfXwyVyk7y8PNTW1nYZ/6WhjWe4RJJUVlbiiy++gBACn376Kd59913Exsa6OxZJxH+a0R0pJSUFx48f7zF9yZIlWLp0qUu2eePGDbzwwgu4cuUKAgMDsXjxYsTExLhkW6RMHFIgIpKEQwpERJKwcImIJGHhEhFJwsIlIpKEhUtEJAkLl4hIkv8Dd6zdsltuSFcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# item_Visibility distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.displot(big_mart_data['Item_Visibility'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "SvoxA08SvxG3",
        "outputId": "67c3d9b3-78b9-4340-d480-358af88618a0"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# item_MRP distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.displot(big_mart_data['Item_MRP'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "ru7k5r8Tv60x",
        "outputId": "52e7ae0c-9666-40a2-f81b-2d4be8d73503"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# item_Outlet_Sales distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.displot(big_mart_data['Item_Outlet_Sales'])\n",
        "plt.show()\n",
        "# This is a normal distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "5HERpVcHwIHj",
        "outputId": "bd012be5-e760-4a7d-b9f5-edad6762ccce"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Outlet_Establishment distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='Outlet_Establishment_Year',data = big_mart_data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "_DXIUFU_wY3N",
        "outputId": "1668ea5f-ec45-439d-ecb8-0614b5ad69d1"
      },
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Categorical features"
      ],
      "metadata": {
        "id": "ewZRAkGlzpYO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Item_Fat_Content distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='Outlet_Establishment_Year',data = big_mart_data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "5X9-pEFuzogV",
        "outputId": "5b39e22d-71cb-4d29-8e4e-589567b0513d"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Item_Tyoe distribution\n",
        "plt.figure(figsize=(30,6))\n",
        "sns.countplot(x='Item_Type',data = big_mart_data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "Wxq9YDT30A6h",
        "outputId": "3ef41e81-1cd5-4bd2-b4ea-09a85fd3ad03"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x432 with 1 Axes>"
            ],
            "image/png": 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bt2bcuHF5//vf3/pgwZ3/v707Daiq2tsA/hwBwYICycwSSy3kWiA5ICoocFERESRFtJTUWyROZM5QTpUaTiWKZlmmOaRiKNJ01RxSQM3ZArrkRczEAUEQDtP5vx942ZcD5xxAEFGf3yc4e1p77zXttfdaa8MGnc9ap0+fxuLFi5UeaRMnToSbmxsuXbpUqU21WbNmD1QaqiuG0qK+66uvja+wsBBBQUFQq9Xw9/eHv78/RowYUadpo8ERalDc3d0lOTlZ67effvpJ+vXrJ1lZWVJcXCwiIteuXRNXV1fJysoSEZHhw4fLvn37RERk+vTp4u/vL7dv3xYRkZs3b0r37t0lNzdXRERWrFghH374YX2dUoNSUlIiISEh4uTkJBMmTJAvv/xSMjMzRUTk+vXr0rVrV/njjz9ERGTr1q0yePBgZVtbW1vlGi5fvlwWLlyo9zj6lnfs2FFOnz4t6enpYmtrK3FxcTq3iY6Ols6dO8vly5dFRCQ8PFyWLl2qLJswYYL8/vvv4uPjI8eOHROR0ngyevRoZX9lceNh5e7uLklJSeLl5SVZWVnyySefyPr162X69OmyYcMGWblypaxcuVJZPyIiQrnGmZmZotFoREQkNTVVXF1dtfZbMY3eS+7u7tK3b1/x9fUVX19fOXjwoERHR4ujo6OkpaVprVc+3OX/L/93+bxERGTMmDESGxsrIiIajUays7MrhUGj0SjpSKPRyNSpU2XTpk0iYjgur1+/XkaNGiWFhYWSl5cn/v7+MmHCBJ3n+eWXX8rEiRNFpDQdu7m5yYkTJ+TYsWPy5ptvSkFBgYiI7N+/XwIDA5X9jx49WoqKikStVktAQICy/5iYGHn33XelpKREREQ2btwo77zzjohUTr9VnZ+9vb2kpqaKiEhkZKRyjLK0KiIGw1mda3wnKsYNX19fcXBwkISEBAkNDZVly5aJiEhGRob06NFDiQPl87ry/+vLYwydm6H7v3HjRnn99deloKBACgoKJCgoSDZu3CgioqRTEZEFCxbIjBkzRETkxo0b0qtXL4P5b0NR/vo7OjrK+PHjlWXVKc/XrFkjr7/+uty6dUtEtONTQkKCtG/fXs6fPy8iIlFRUUr83bt3rwwYMEBu374tJSUlMm7cOPH3968UvuvXr4utra3B+LZ//37p37+/5OTkKHE/IiJCRErzzGnTpolGo5GcnBzx9vaW/fv3i4iIv7+/7NixQ0RETp48KXZ2drJv3z65efOmdOrUSfLz80VEJCcnR4qKiu7wCtcdQ3UDkdI0UBYfjx8/Li4uLsqyGzduKH8vXbpUFi1aJCKl98jT01OuXr0qIiK5ubmiVqslPT1dnJyc5ObNmzJ8+HD56quvdIYpPT1d/vGPf2il38zMzIfmnpRXnXKuqjpcmcmTJyt1YEP1gOnTp8vQoUNFrVZLQUGBeHt7yy+//KIzfLa2tuLj46OELykpSZKTk6VHjx6SkZEhIiLLli2T0NBQETGc940fP14iIyNFROTixYvi6OioxL2OHTsq+ysoKJC8vLxaXNV7Q1++uHz5cunVq5eSntLS0mTIkCGSk5MjIiIpKSnSq1cvERFRq9VKGVVYWCgjRoyQAwcOKPtZuHChHD58WHx8fJT4UHbssnLOULmur0wuXy7VF0PluIhIWFiYfPvttyJSmo9NmjRJvvnmGxHRzpsOHz4sAQEByv/ly/mK9cPly5dL7969JTs7WzQajYwaNUrZZ0WG8j9DZZSPj4/k5uZKcXGxvPXWWzrLKJHSa+7q6qp1/s7OzrJ8+XIREdm0aZNSP9BoNNK7d2/5/fffpaCgQFxdXbWekWxtbXXWe0eOHClHjx4VkdJ0NWzYMCWtu7u7K/WN9PR0cXR0lNzcXCkqKhJ/f3/5z3/+IyKl+WafPn2U/+tCVfe+qmeV2bNnK//XpB5lqC1CpHJarbjN8OHDJTQ0VEpKSuTWrVvi5OQkFy5cUJbpqt/crXpwQxEXFye+vr46l+Xk5Cj159zcXOnXr58SjwyVQ4aWGcoX9NUF6H/udjllKD0OGDBATpw4ISIixcXFyr7vV9WpX5d/5qxJvhYZGSnjxo1T0k/ZfTH07KmvzKgoISFBHBwclLy3rD5pqP6m79m6oKBAXFxclLw7Li5OKY/Onz8vXl5eSnvEg9J+t2/fPp153vnz58XJyUnrmbaMvmet7Oxs8fPzU+q/GRkZ4urqKtnZ2TrbVB+0NFRX9KVFQ9fXUF257Hm2TF2njYaGPc7uA/L/b10zMzMRFhaGtLQ0GBkZITs7GxcuXICjo2Olbby8vPDII48AACwtLeHh4YGdO3diyJAh2LZtW7Xm0noQNWrUCFFRUUhJScGxY8ewZ88erF27FrGxsTh9+jTs7OyUHkSDBg3C3LlzkZubq3wdVltS7g26qakp+vXrp3fdjh07okWLFgCADh06aA2dlZycjPHjx+PTTz9Vvt60s7NDamoq5s6dCycnJ7i5udVJmO9nKpUK/fr1Q1xcHOLi4rBlyxacP38eALBv3z7k5ubixx9/BFD65b+dnR2A0mEAJ0+ejIyMDBgbG+P69eu4du1apR5/DUXFIax27NiBjh07olWrVrXed9euXbFq1SpcvHgRPXr00DlkrEajwRdffIGDBw9Co9EgOzsbZmZmynJ9cTkxMREDBw6EiYkJTExM4Ovrq3ceBV9fX3zyySfIysrCb7/9hiZNmuDll19GREQEkpKSEBAQAKA0jd26dUvZv5+fH4yNjWFsbIz+/fvj119/BVB6/8+dOwd/f38AUHpK6VLV+XXq1EnpXRUQEIABAwZU2se+ffv0hrM61/hOVYwbZfOIxMfHY8aMGQBKhzTt1asXEhMTDQ6Fpi+PMXRugP77Hx8fD39/f6Xn3iuvvII9e/bg1Vdf1TpuYmKi8iVa06ZN0bt37zu+HvWt7PoXFBRgwoQJWLduHUaOHFlleR4ZGYmnn34aa9as0Tt8SevWrZUvxRwdHZU5KhITE9GvXz+lDjBw4EBERUVVK7xDhw5Fbm4uTE1NER0djfj4eHh7eytpY8iQIcr8UvHx8QgLC4NKpYK5uTn69++P+Ph4dOrUCSkpKfDz81PCVhavLCws0KpVK0ybNg0uLi5wc3Ors/K1NgzVDcq+zCsbTsTR0RFXr15FQUEBTE1NsXPnTsTGxqKoqAh5eXl47rnnAAD79++Hn5+fUm6UH8avsLAQr776KiZMmGCwHqBrqMaH5Z5UVFU5V5063Mcffwy1Wq3kfYbqAQDg6empfCnbvn17JY/WpeJQjRs2bECvXr3w5JNPAihNW2XX31DeVz6/s7GxUb60BgBnZ2fMmDED7u7ucHNzg42NzR1dy3tNV74IAD179kTTpk0BAIcOHcLFixfx2muvKdsVFxfj+vXreOSRRxAREYGTJ09CRHD9+nUkJSWhZ8+eAIBffvkFhw4dwtq1a3X2/gYMl+t3s0y+E/rKcaA0Dp85cwZffvklAECtVivnfO7cOXz66afIzs6GSqXCf//732of08XFRel16uDgoHf+Kn35H2C4jPL29lbSy+DBgw2WUcHBwUovLwBacwH5+flh5cqVyMrKwpkzZ2BtbQ07OzskJSXBzMwMnTt3BgD07t1bZy/avLw8HD16FJmZmcpvt2/fRmpqqpLWy/L+li1b4rHHHsOVK1cgIkhNTcU777yjbFdUVIQ///yzTnuBGrr3VT2rlO8RWdt6lFT4Arx8WtXFy8sLjRo1goWFBdq2bYuLFy8qcUNX/aahpbn6pFarMWfOHCQnJ0OlUuHq1atISkpS4pGhckjfMn35Qm5urt66AGm7m+WUofTo7OyMBQsWoE+fPujZs+d9f3+qU78uryb52s8//6z0vASglSfpe/bUV2boomuoRkP1N33P1hqNBk2aNFF6PXl7e2PWrFkASut5xcXFCA8PR9euXeHu7l7DK9wwVSwzqkvXs9bJkydx6dIlZQQNoLSNLy0tDVZWVpXaVB+0NFRX9KXFadOm6b2+7du3N9gGVl5dp42Ghi/O7gNnz57FCy+8gDlz5sDDwwMrVqyASqVC37599XavL2swKzN8+HBMmTIF1tbWaNu2rdaDzcOobCiq1157Dd7e3jh69OhdHw/3zz//REFBAdq0aYOsrCw0adLE4JA/ZRVhADAyMkJJSYnyf/PmzXH79m0kJiYqFWsbGxvs3r0bCQkJOHjwIJYtW4bY2Fit/TyM/P39ERAQgC5dusDKykr5XUQwe/ZsrUapMu+88w5mzJgBT09PaDQadOjQ4b4byqLiXCtGRkbQaDTK/9U9n5EjR8LDwwNHjhzB+++/jx49emDSpEla68TGxuLXX3/Fxo0bYW5ujtWrV2s10BiKy9XVtGlTuLi4YPfu3Th58qTSeCAiGDRoEEJDQ2u0PxFBSEgIBg8eXOW6VZ1fdY+nL5zVucb1ycjISKnwlo8n+vKYqu5BXdz/+52pqSnc3Nywf/9+jBw5ssry3NHREYcPH8bly5f1ltflX6g1atQIxcXFNQqTtbU1mjdvjrNnzyqNMFu2bEFKSooyTFVdMzIywtatW3HixAkkJCTglVdeweeff673obW+6aob9OnTB8D/4nHZUKLFxcU4e/YsNm/ejC1btqBp06aIjY3F1q1bqzyOiYkJOnTogH379qFPnz73dG6uhn5PDKnJnGLbt2/HkSNHsH79emX4Q0P1AKDh5V0rVqzA2bNnkZCQgKCgIMyZM0cZvvh+VD5ftLe3r3Q/XV1dERERUWm7lStX4tatW9i2bRtMTU3x3nvvaeWfrVu3xh9//IFz587pfXFmqFxvaGWyISKCqKioSi9RCwsLERoaiq+//hovvvgiMjIylBeL1VEx7uuqMx4/ftxg/lfbMqo6HnnkEQwYMAA7duzA0aNHtRqwq6NsnqLt27frnWdKVz6gUqlgZWV1T+egrOpZpWJbQG2UtUWUqSrvNZR36qrf3E9p7k60b98eaWlpyM7OVoZyLrN06VI0a9YMCxcuhLGxMUaPHq11Hw1dS33L9OULubm5dXpeD4O7VU7pExYWhuTkZCQkJCA0NBSjRo3CkCFD6ux87hVD9evy6ipf05c2altm1JWyNkALCwvExcUhMTERR44cweLFi/Htt9822I+1q8vW1hYXL15EVlaW1gvSU6dOoV27dnq30/WsJSJo164dNm7cWGn9S5cuVWpTfVDTUF2pmBYNXd+YmJhat4HVVEOdN/LBn8XtPrdnzx5s3rwZo0ePRk5ODp555hmoVCocPnwYaWlp1d5Pu3btYGlpifnz51f6mv9hkpGRgZMnTyr/X7lyBZmZmWjZsiUcHR2RlJSE1NRUAKVz9rRv317nl9fm5ubIycmp9nEvXbqE8PBwDBs2rE6+5La0tMS6deuwa9cu5WuyK1euwMjICJ6enpg5cyYyMzORlZVV62Pd72xsbDBp0iSMHTtW63cPDw+sW7cOarUaQOnDRNm9z8nJQcuWLQFAmbi5zKOPPlqje99QtGrVSpn3Jj4+HtevX9e5XsXzu3DhAlq1aoWhQ4ciKChI2Ud5OTk5sLKyUtLF7t27qxUmZ2dn7Ny5E8XFxVCr1VVuN2jQIGzevBn79+9Xvjgr60175coVAKU9x86dOwcAcHJyQmxsLIqLi1FQUIDvv/9e2ZeHhwc2bdqE7OxsAKWNTElJSQAqp++qzu/EiRNKJSI6OhrOzs6Vwm4onNW5xnWtW7duSgPXtWvXcODAASXc5eNKbGysso2+PMbQuVUVhpiYGBQVFaGoqAgxMTHo3r17pfWcnZ2VL/5u3ryJPXv21O7k7wGNRoNjx44pjURVleeurq6YM2cOgoOD8ccff9ToWE5OTvjxxx+Rn58PjUajzIWly7hx47BgwQJcuHBB+S0/P1/5u1u3bvj++++Rm5sLEcH27duVe9StWzdER0dDRJCbm4vvvvsO3bt3h7m5OWxtbZW4c+bMGaSkpAAozWczMzPh5OSEiRMnwtbWtsbndzcYqhsYcuvWLZibm8PS0hKFhYWIjo5Wlrm5uWHnzp1KXls2zwVQ+lAwf/58mJubY9KkScpcDtXxsNyTmjJUhzty5Ag+++wzrFq1SutLSUP1gNrq2rUrDhw4gGvXrgEAtm7dqnWf9OV9zs7OSjxKT09HfHw8gNLGg/T0dDg4OCA4OBg9evSo1aTeDUHFfLG8Hj164NChQ1px8cyZMwBK889mzZrB1NQUGRkZ2Lt3r9a2zzzzDL744gssXboU3333nc5jGyrX9ZXJNa371wcPDw+sWbNGaRTMzMxEeno6CgsLUVxcrHxxv2nTJr37uNN6raH8zxBnZ2d8//33yMvLQ0lJSbW30+fVV1/FV199hXPnzikNsW3atEF+fr4yysCePXu0esKXMTc3R6dOnbBmzRrlt7///ltJt/q0bt0aZmZmiImJUX5LTU2t1xcThp5VKqpNPap8W0Rd0FW/uRf14Pr03HPPwcPDA7NmzVLiSElJCbZt24acnBw89dRTMDY2RkpKCo4fP17r4+nLFwzVBUi3u1FOGUqPf/75J9q1a4fXX38dvr6+931aqKp+/eijj2rlmzXJ19zd3fHVV18p65TvOWyIrjKjugzV3/Q9W7dp0wZqtVpJ2z/88INSHmVmZiI/P1+ZD8rCwgLp6ek1ClND1LJlS3h5eWHOnDnKs09KSgpWr16N8ePH16g+9fLLLyMtLQ0JCQnKb2fOnNHbq+1BS0N1RV9afP755/Ve35q08dV12mho2OOsAZo4cSIaN26M/Px8tG3bFmvWrEGHDh0wefJkzJ07F5GRkbC3tzf4tl6XgIAALFu27IHpAnwniouLERkZib/++gtmZmbQaDR4++23laFEIiIiMGXKFBQXF6Np06ZYtGiRzv14enoiJiYGfn5+6N+/P4KDgyutExMTg/j4eOTn58Pc3BwDBgzAiBEj6uxcLCwssHbtWowZMwZ5eXl46aWXlEm9NRoNgoOD9X5p+7AJDAys9FtwcDBWrFiBwYMHQ6VSQaVSYfz48Wjbti1mzpyJsWPH4vHHH4erq6vWlzJBQUEICwuDueyG/wAACqhJREFUmZkZlixZogwL1dCFhoZixowZ+Prrr+Hs7Iynn35a53qBgYFYuHAh1q5di+nTp2PPnj1ITEyEiYkJGjdurAwtUd7AgQOxd+9eeHl5wdraGp06darWV3VDhgxBcnIyvL29YWVlBXt7e9y4cUPv+q6urnjvvffg5OSkTKLcpUsXvP322wgJCUFJSQmKiorg5eWFl156CUOHDkVSUhL69+8PKysrZTjFsjBnZWUpw/+ICIYNGwY7O7tK6XvYsGEGz69jx4746KOPkJaWhieeeEJnvmEonBs2bKjyGte1d999F7NmzVKGlZwyZYryNfHMmTMxa9YsWFhYwMvLS9kmOTlZZx7TvHlzvedmSGBgIC5evKgMl+ni4qLzi7CxY8ciLCwMXl5eaNasmTL00v1g4sSJMDU1RVFREV544QWMGzcOAKpVnnfr1g0LFixASEgIli9fXu1j/vOf/8TJkyfh6+uLxx9/HI6OjsoL4ooCAwNhZmaGyZMnIzc3F02bNkWTJk2U4RR69eqF5ORkDB06FADw0ksvISQkBEDpfXn//feVOOTr66v0aIiIiMDMmTPx2WefwdbWFvb29gBKX0xMmDABarUaIoL27dvX+KH1bqiqbqCPq6srdu3ahb59+8LKygqdO3dWHtC6du2K4OBgjBo1CiqVCo0bN8bq1auVbVUqFWbPno2PPvoI48aNQ2RkZLV6iD8s96SmmjZtqrcOt2rVKuTl5Wk1/G7cuNFgPaC2bG1tMWXKFOWYNjY2mDdvHgDDeV94eDimTZuG3bt3o2XLlsrwJRqNBjNmzEBOTg5UKhVatGiByZMn1zqc94KufHH9+vVa6zz33HNYtGgRwsPDoVarUVRUhI4dO8LBwQEjRoxAaGgofHx80Lx5c509Blu0aIF169bhX//6F9RqtdYQd4Dheou+MtnX1xczZ87EDz/8gFGjRmkNGXWvhIWFYdGiRfDz84NKpYKJiQnCwsJgY2ODiRMnYvDgwbC0tETfvn317qNivba6DOV/hri7u+PUqVPw8/PDY489BicnJ2RkZFT7uBXZ2NigTZs2cHBwUHq5NW7cGEuWLMGcOXMAlH5QYm1tDQsLi0rbL168GAsWLFDyzUcffRQffvihwS/+jY2NsXr1asyfPx9r166FRqOBtbU1Pv744zs+j5oy9KxSUU3rUfraIupKxfrN9u3b670eXN8WLlyIlStXYtCgQTAxMYFGo0GvXr3w5ptvIiwsDNu3b0fr1q3RpUuXWh/LUL6gry5A2u5mOWUoPS5ZskQZxv2xxx7Dhx9+WG/nfDdUVb8ePXo0goKCYGZmhg0bNtQoXwsODsaSJUuUaR+effbZaj0v6SozqstQ/c3Qs/XSpUsxd+5cAKVtAmXtMH///Tfee+89FBcXo6SkBD179tQ5Dc/9aPbs2Vi6dCm8vb1hYmICU1NThIeHw8nJCTk5Ofjiiy/g6+sLJycng3n+448/jqioKCxatAjz589HUVERbGxstJ6pynvQ0lBdMZQW9V3fmrTx1XXaaGhUcqcDkNJ9Jzw8HK1bt8Ybb7xxr4NCRFQvyua3KSwsREhICLy8vJS5uIgeVGXxXqPRIDw8HE8++eQDNewRERERUFreeXl5ITo6WuuDwfLzGyYkJGDmzJnYu3evMlQrERE9fPSVGURE+rDH2UMgIyMDQUFBaNas2QP5BRcRkT6jRo1CYWEhCgoK0L17d+UrGKIH2fTp0/HXX39BrVbjxRdf1Jrwl4iI6EGwefNmrFq1CqNHj67UAPrTTz9h3bp1EBE0btwYixcv5kszIqKHmKEyg4hIH/Y4IyIiIiIiIiIiIiIiIgLAz66IiIiIiIiIiIiIiIiIwBdnRERERERERERERERERAD44oyIiIiIiIiIiIiIiIgIAF+cEREREREREREREREREQEAjO91AIiIiIiIiB5mHh4e+OCDD3DlyhVs27YNmzdvrrdjz5o1C7GxsQCAoqIiiAgaN24MAOjUqRM+//zzegsLERERERFRQ8AXZ0RERERERA+pefPmYd68eQCAyMhIpKWlYfHixfc4VERERERERPcOh2okIiIiIiK6x1JTUzF79mycOnUKL7/8Mjp37gwAKCwsxEcffQQ3Nzd0794ds2bNglqtBgAkJiaiZ8+e+Oyzz9CtWze4uLhgz549OHDgAPr27QsnJyesXr36jsIzd+5cLFy4UOu3MWPGYN26dQBKe8l9+umn8Pb2RpcuXTBz5kwUFBQo6/7888/w8/ND586dMXToUCQlJd1ROIiIiIiIiOobX5wRERERERHdY23btsXcuXPh6OiIkydP4vjx4wCAxYsX48KFC4iJicFPP/2Eq1evYuXKlcp2169fR0FBAQ4ePIiJEyfi3Xffxa5duxAdHY2NGzciKioK6enpNQ6Pv78/du/eDY1GAwDIzMxEfHw8fHx8lHViY2Oxdu1a/Pvf/8aFCxcQFRUFAPjtt98QFhaGefPmITExEYGBgRg7diwKCwtrc4mIiIiIiIjqBV+cERERERERNUAigq1btyIsLAyWlpYwNzfHW2+9hbi4OGUdY2NjhISEwMTEBN7e3rh58yaCgoJgbm6OF154Ac8//zySk5NrfGwHBwdYWFggPj4eAPDdd9/ByckJTzzxhLLOa6+9hhYtWsDS0hIhISFKuL755hsEBgaiQ4cOMDIygr+/P0xMTHDq1KlaXhEiIiIiIqK7j3OcERERERERNUCZmZnIz8/HK6+8ovwmIkovMACwtLSEkZERAMDMzAwAYG1trSw3NTXF7du37+j4/v7+2LVrF3r06IFdu3YhKChIa3mLFi2Uv59++mlcvXoVAHD58mXExMTg66+/VpYXFRUpy4mIiIiIiBoyvjgjIiIiIiJqAFQqldb/VlZWMDMzQ1xcHJo3b17v4fH19YWPjw+SkpKQmpoKT09PreV///238vfly5fx5JNPAih9oTZmzBiEhITUa3iJiIiIiIjqAodqJCIiIiIiagCsra2RkZGhzAXWqFEjBAQEYP78+bhx4wYAICMjA4cOHaqX8Dz11FOwt7fH1KlT0adPH6VHW5lNmzbhypUryMrKwurVq+Ht7Q0ACAgIwJYtW3D69GmICPLy8rB//37k5ubWS7iJiIiIiIhqgy/OiIiIiIiIGgBnZ2c8//zzcHFxQdeuXQEAU6dOxbPPPoshQ4agY8eOGDlyJC5cuFBvYRo4cCBSUlLg5+dXaZmPjw9Gjx4NT09PtGrVSulhZm9vj/fffx/z5s1Dly5d0KdPH+zYsaPewkxERERERFQbKhGRex0IIiIiIiIianiOHTuGqVOn4ueff9YaStLDwwMffPABunfvfg9DR0REREREVPfY44yIiIiIiIgqKSoqwvr16zF48OBK868RERERERE9qIzvdQCIiIiIiIjo7nrjjTfw66+/Vvr9rbfewpgxYyr9npqaikGDBsHOzg4LFiyojyASERERERE1CByqkYiIiIiIiIiIiIiIiAgcqpGIiIiIiIiIiIiIiIgIAF+cEREREREREREREREREQHgizMiIiIiIiIiIiIiIiIiAHxxRkRERERERERERERERASAL86IiIiIiIiIiIiIiIiIAPDFGREREREREREREREREREA4P8Ap8os4ih9ny8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Pre-Processing"
      ],
      "metadata": {
        "id": "74101tox0ytv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "big_mart_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "BnJrk-0B0ROF",
        "outputId": "6b3a6835-8648-46af-f7f1-2cef321d3ea4"
      },
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
              "0           FDA15         9.30          Low Fat         0.016047   \n",
              "1           DRC01         5.92          Regular         0.019278   \n",
              "2           FDN15        17.50          Low Fat         0.016760   \n",
              "3           FDX07        19.20          Regular         0.000000   \n",
              "4           NCD19         8.93          Low Fat         0.000000   \n",
              "\n",
              "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
              "0                  Dairy  249.8092            OUT049   \n",
              "1            Soft Drinks   48.2692            OUT018   \n",
              "2                   Meat  141.6180            OUT049   \n",
              "3  Fruits and Vegetables  182.0950            OUT010   \n",
              "4              Household   53.8614            OUT013   \n",
              "\n",
              "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
              "0                       1999      Medium               Tier 1   \n",
              "1                       2009      Medium               Tier 3   \n",
              "2                       1999      Medium               Tier 1   \n",
              "3                       1998         NaN               Tier 3   \n",
              "4                       1987        High               Tier 3   \n",
              "\n",
              "         Outlet_Type  Item_Outlet_Sales  \n",
              "0  Supermarket Type1          3735.1380  \n",
              "1  Supermarket Type2           443.4228  \n",
              "2  Supermarket Type1          2097.2700  \n",
              "3      Grocery Store           732.3800  \n",
              "4  Supermarket Type1           994.7052  "
            ],
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              "      <td>FDA15</td>\n",
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              "      <td>FDN15</td>\n",
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              "      <td>OUT049</td>\n",
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              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
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              "      <td>NCD19</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>994.7052</td>\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "big_mart_data['Item_Fat_Content'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdF9r3Bfjqeb",
        "outputId": "bd43b63d-ea20-4534-c3e9-bcdfee3d7ea1"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat    5089\n",
              "Regular    2889\n",
              "LF          316\n",
              "reg         117\n",
              "low fat     112\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Label encoding"
      ],
      "metadata": {
        "id": "6vhtEblvkRQv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = LabelEncoder()"
      ],
      "metadata": {
        "id": "e7s6csdlkQlX"
      },
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "big_mart_data['Item_Identifier'] = encoder.fit_transform(big_mart_data['Item_Identifier'])\n",
        "\n",
        "big_mart_data['Item_Fat_Content'] = encoder.fit_transform(big_mart_data['Item_Fat_Content'])\n",
        "\n",
        "big_mart_data['Item_Type'] = encoder.fit_transform(big_mart_data['Item_Type'])\n",
        "\n",
        "big_mart_data['Outlet_Location_Type'] = encoder.fit_transform(big_mart_data['Outlet_Location_Type'])\n",
        "                                                              \n",
        "big_mart_data['Outlet_Type'] = encoder.fit_transform(big_mart_data['Outlet_Type'])\n",
        "\n",
        "big_mart_data['Outlet_Identifier'] = encoder.fit_transform(big_mart_data['Outlet_Identifier'])\n",
        "\n",
        "big_mart_data['Outlet_Size'] = encoder.fit_transform(big_mart_data['Outlet_Size'])\n",
        "\n"
      ],
      "metadata": {
        "id": "ZlZLF3mfj4XF"
      },
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "big_mart_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "8YRb44hgkxDa",
        "outputId": "00ccdba4-f260-4a3e-fe49-9b5df6f1c9fd"
      },
      "execution_count": 224,
      "outputs": [
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            "text/plain": [
              "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
              "0              156         9.30                 1         0.016047          4   \n",
              "1                8         5.92                 2         0.019278         14   \n",
              "2              662        17.50                 1         0.016760         10   \n",
              "3             1121        19.20                 2         0.000000          6   \n",
              "4             1297         8.93                 1         0.000000          9   \n",
              "\n",
              "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
              "0  249.8092                  9                       1999            1   \n",
              "1   48.2692                  3                       2009            1   \n",
              "2  141.6180                  9                       1999            1   \n",
              "3  182.0950                  0                       1998            3   \n",
              "4   53.8614                  1                       1987            0   \n",
              "\n",
              "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \n",
              "0                     0            1          3735.1380  \n",
              "1                     2            2           443.4228  \n",
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              "4                     2            1           994.7052  "
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting features and Target"
      ],
      "metadata": {
        "id": "NkyIlzrxoovW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = big_mart_data.drop(columns='Item_Outlet_Sales',axis=1)\n",
        "Y = big_mart_data['Item_Outlet_Sales']"
      ],
      "metadata": {
        "id": "ChxDcMaXosZp"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9M-z9vmpKJK",
        "outputId": "3fc922bc-1cb3-464b-a337-33608fe3c5a1"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  \\\n",
            "0                 156        9.300                 1         0.016047   \n",
            "1                   8        5.920                 2         0.019278   \n",
            "2                 662       17.500                 1         0.016760   \n",
            "3                1121       19.200                 2         0.000000   \n",
            "4                1297        8.930                 1         0.000000   \n",
            "...               ...          ...               ...              ...   \n",
            "8518              370        6.865                 1         0.056783   \n",
            "8519              897        8.380                 2         0.046982   \n",
            "8520             1357       10.600                 1         0.035186   \n",
            "8521              681        7.210                 2         0.145221   \n",
            "8522               50       14.800                 1         0.044878   \n",
            "\n",
            "      Item_Type  Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  \\\n",
            "0             4  249.8092                  9                       1999   \n",
            "1            14   48.2692                  3                       2009   \n",
            "2            10  141.6180                  9                       1999   \n",
            "3             6  182.0950                  0                       1998   \n",
            "4             9   53.8614                  1                       1987   \n",
            "...         ...       ...                ...                        ...   \n",
            "8518         13  214.5218                  1                       1987   \n",
            "8519          0  108.1570                  7                       2002   \n",
            "8520          8   85.1224                  6                       2004   \n",
            "8521         13  103.1332                  3                       2009   \n",
            "8522         14   75.4670                  8                       1997   \n",
            "\n",
            "      Outlet_Size  Outlet_Location_Type  Outlet_Type  \n",
            "0               1                     0            1  \n",
            "1               1                     2            2  \n",
            "2               1                     0            1  \n",
            "3               3                     2            0  \n",
            "4               0                     2            1  \n",
            "...           ...                   ...          ...  \n",
            "8518            0                     2            1  \n",
            "8519            3                     1            1  \n",
            "8520            2                     1            1  \n",
            "8521            1                     2            2  \n",
            "8522            2                     0            1  \n",
            "\n",
            "[8523 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k975bAP_pMSF",
        "outputId": "0552e105-aaa9-4602-fdc1-91e0fed0bc9d"
      },
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       3735.1380\n",
            "1        443.4228\n",
            "2       2097.2700\n",
            "3        732.3800\n",
            "4        994.7052\n",
            "          ...    \n",
            "8518    2778.3834\n",
            "8519     549.2850\n",
            "8520    1193.1136\n",
            "8521    1845.5976\n",
            "8522     765.6700\n",
            "Name: Item_Outlet_Sales, Length: 8523, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the data into Training Data and Test Data"
      ],
      "metadata": {
        "id": "vt1MtHmHpRcX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=2)"
      ],
      "metadata": {
        "id": "7_zmcqr1pVg5"
      },
      "execution_count": 228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape,X_train.shape,X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGmB9Hgppwhk",
        "outputId": "20a10c20-5edb-4163-b909-f68c900bcacd"
      },
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8523, 11) (6818, 11) (1705, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Machine Learning Model training "
      ],
      "metadata": {
        "id": "dGrk8CYRp7yJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "XGBoost Regressor"
      ],
      "metadata": {
        "id": "Kz-fwmLQqATw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "regressor = XGBRegressor()"
      ],
      "metadata": {
        "id": "nY9ihj3mp_F4"
      },
      "execution_count": 230,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NROqik4KqKh1",
        "outputId": "a5592cfb-34cb-460e-f01f-de53e4f00e64"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[09:54:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluation"
      ],
      "metadata": {
        "id": "nww6PLjnqZJa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prediction on training data\n",
        "training_data_prediction = regressor.predict(X_train)"
      ],
      "metadata": {
        "id": "Bm1qatREqgBD"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# R square Value\n",
        "\n",
        "r2_train = metrics.r2_score(Y_train,training_data_prediction)"
      ],
      "metadata": {
        "id": "mcgz23wkqov5"
      },
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('R Squared value : ' ,r2_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nS0-6PRvq7aU",
        "outputId": "71b2fe68-f001-4eab-9089-1e0fedfa8e9d"
      },
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Squared value :  0.6376677682298715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prediction on training data\n",
        "test_data_prediction = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "J2mm443urRYe"
      },
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# R square Value\n",
        "\n",
        "r2_test = metrics.r2_score(Y_test,test_data_prediction)"
      ],
      "metadata": {
        "id": "d-pSpQyFrXm3"
      },
      "execution_count": 236,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('R Squared value : ' ,r2_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oIRohunNrd09",
        "outputId": "f40e7e3f-fb7f-42e3-8359-0b9231542ef1"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Squared value :  0.5881605982886744\n"
          ]
        }
      ]
    }
  ]
}